Good Bots vs Bad Bots of Twitter

Elon Musk's recent move to impose restrictions on content viewing on Twitter has reignited massive interest in social media bots. However, amidst the concern surrounding these automated dwellers of the social media universe, there exist numerous misconceptions. It is crucial to acknowledge that while the landscape of bots on Twitter is riddled with bad actors, there are also plenty of “good bots” that serve legitimate purposes. ZENPULSAR uses millions of daily tweets to train our NLP models, and using our high-precision bot detection mechanism, we can clearly observe how bad and good bots affect the overall Twitter discourse.

What Are Bad Bots?

Bad bots on social media platforms like Twitter are typically designed with nefarious intent, seeking to exploit vulnerabilities and manipulate various aspects of online interactions. Here are five examples of bad bots commonly found on Twitter:

1. Spam Bots

Spam bots flood Twitter with repetitive or unsolicited content, such as promotional links, advertisements, or low-quality messages. They aim to deceive users, disrupt conversations, and drive traffic to external websites, often leading to phishing attempts or spreading malware.

2. Fake Engagement Bots

Fake engagement bots create fake accounts and engage in artificial interactions, such as likes, retweets, or replies. Their purpose is to artificially boost the popularity or influence of specific accounts, deceiving users and distorting the authenticity of online engagement.

3. Troll Bots

Troll bots are programmed to engage in provocative, offensive, or inflammatory behavior on Twitter. They often target individuals or groups, posting harassing comments, spreading misinformation, and instigating conflicts to disrupt conversations and sow discord. In certain cases, the intent of troll bots is quite similar to that of fake engagement bots – they aim to drive engagement in the form of comments by igniting “comment wars”.

4. Hashtag Manipulation Bots

These bots operate in coordinated networks, aiming to manipulate trends, hashtags, or topics on Twitter. They artificially amplify certain discussions or hashtags to sway public opinion, spread misinformation, or create a false sense of popularity or support. These bots are often instrumental in amplifying social media-based panic waves. Such panic waves could bring entire businesses or financial assets down - something that we recently witnessed in case of the Silicon Valley Bank collapse.

5. Identity Misrepresentation Bots

Identity misrepresentation bots create fake accounts or impersonate real users, often with the intent of deceiving others, engaging in scams, or spreading misinformation. They can cause reputational damage, privacy breaches, and financial losses.

What Are Good Bots?

Although news headlines have typically concentrated on bad Twitter bots, not all bots on the platform serve sinister purposes. Contrary to their malicious counterparts, good bots on Twitter serve beneficial purposes and provide significant contribution to a positive user experience. These bots are designed to automate tasks, provide assistance, or deliver useful information. They adhere to platform policies and respect user privacy. Here are five common examples of good bots on Twitter:

1. Customer Support Bots

Customer support bots assist users by providing instant and automated support, answering common queries, and guiding individuals through basic troubleshooting steps. They enhance user experience and ensure timely responses. Though Twitter is typically not the first source that people use for support enquiries, the platform’s highly interactive, nearly chat-based format lends itself well to customer care interaction.

2. News Aggregator Bots

News aggregator bots curate and share relevant news articles or updates from trusted sources, helping users stay informed about current events, breaking news, or specific topics of interest. They contribute to the dissemination of reliable information.

3. Content Recommendation Bots

Content recommendation bots analyse user preferences and behavior to suggest relevant content, including articles, videos, or accounts to follow. They personalise the user experience, enhance engagement, and help users discover new content. Throughout the social media world, including on Twitter, these bots have been crucial for many years in delivering personalised and relevant content.

4. Language Correction Bots

Language correction bots assist users by providing real-time suggestions and corrections for spelling, grammar, or syntax errors in their tweets. They promote effective communication and help users improve the quality of their written output.

5. Data Analysis Bots

Data analysis bots collect and analyse data from Twitter, providing valuable insights into user behavior, trends, or engagement patterns. While you cannot expect deep insights from these bots, they are often useful to quickly get daily quotations, market prices, or other bits of basic market information.

The ongoing hype and hysteria surrounding bots on social media platforms should not overshadow the fact that bots can be legitimate and useful tools. While bad bots pose risks such as spreading misinformation, engaging in spam, or perpetuating harmful behaviour, good bots offer tangible benefits like efficient customer support, curated news, personalised recommendations, language assistance, and basic data analysis. When the next time you hear the words “bot” and “Twitter” in the same sentence, don’t necessarily presume the worst – there are good bots and there are bad bots on Twitter. The big question is whether Elon Musk’s decision to impose content viewing limitations on Twitter will lead to a decrease in bad bots on the platform. Musk says it will. Can we trust him?